Classification of Team Behaviors in Sports Video Games

Author

Thurau, Christian ; Hettenhausen, Thomas ; Bauckhage, Christian

Author_Institution

Appl. Comput. Sci., Bielefeld Univ.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

1188

Lastpage

1191

Abstract

This paper considers the application of pattern recognition techniques in modern computer games. Towards the problem of realizing more life-like behavior for artificial game characters, we record the network traffic of online multiplayer games. Dealing with a soccer game, we cluster these data and train HMMs in order to achieve fast and robust recognition of behaviors and actions in the virtual game world. Experimental results indicate that pattern recognition and machine learning provide an auspicious avenue towards more convincing artificial characters

Keywords

computer games; hidden Markov models; learning (artificial intelligence); pattern classification; HMM; artificial game character; behavior action recognition; computer game; data clustering; hidden Markov model; machine learning; network traffic; online multiplayer game; pattern recognition; soccer game; sports video games; team behavior classification; virtual game world; Application software; Computer science; Games; Hidden Markov models; Humans; Laboratories; Pattern recognition; Principal component analysis; Robustness; Telecommunication traffic;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.370

Filename

1699102